Advancements in technology have allowed companies to gather copious amounts of data on consumer behavior and internal processes — more data than they can effectively put to use. In most cases, this creates loads of missed opportunities to learn and apply the data to better connect with consumers and optimize business strategies.

Companies can leverage this surplus of big data, however, by combining it with machine learning to perform faster and more complex analytical tasks than what humans are feasibly capable of. Essentially, big data empowers machine learning and artificial intelligence (AI), and the greater amount of data available, the easier it will be for these systems to learn and function.

What Is AI?

Artificial intelligence (AI) is referred to as intelligence exhibited by machines that mimic cognitive functions normally exhibited by humans, including learning and problem-solving. Many industries have already adopted some form of AI. For example, supply chain and manufacturing lines nowadays commonly use machines and robotics that can think and operate autonomously, or with minimal supervised programming, to perform specific actions.

For several years, machine learning has been used to devise a series of complex algorithms that learn and make predictions from data, also known as predictive analytics. These learning algorithms are commonly associated with a neural network (NN) because they operate similarly to the human biological neural network, having several connections and layers between nodes. These learning algorithms utilize big data to progressively learn in order to accomplish a certain goal and improve upon processes without specific programming telling them what to do.

Data scientists use predictive analytics frequently to gain greater insights and identify hidden trends within data sets. Likewise, engineers and researchers have used machine learning methods to better understand how computers learn to do something that they weren’t programmed to do.

Merging Big Data and AI

The convergence of big data and AI has undoubtedly transformed how businesses value their data and use it to stay ahead of their competitors.

New technological developments have not only permitted businesses to store and transfer greater volumes of data from various sources but they have also enabled capabilities in machine learning and AI that were once impossible due to limited data availability, database storage, and the inability to quickly analyze large sums of data.

The importance of big data is expected to grow in the coming years given that machine learning is currently being tested and implemented at a rapid pace to perform time-consuming, task-oriented jobs, which will free employees to perform more human jobs functions, such as those that focus on communication, as well as the development of new business strategies.

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